Machine Learning for Analog Layout

By Ishan Aphale

In back-end analog/mixed-signal (AMS) design flow, well generation persists as a fundamental challenge for layout compactness, routing complexity, circuit performance and robustness. The immaturity of AMS layout automation tools comes to a large extent from the difficulty in comprehending and incorporating designer expertise. To mimic the behavior of experienced designers in well generation, we can use a generative adversarial network (GAN) guided well generation framework with a post-refinement stage leveraging the previous high-quality manually-crafted layouts. Guiding regions for wells are first created by a trained GAN model, after which the well generation results are legalized through post-refinement to satisfy design rules. The network learns and mimics designers’ behavior from manual layouts. Experiments show that generated wells have comparable post-layout circuit performance with manual designs on the op-amp circuit.

The power of machine learning algorithms has been demonstrated extensively for analog device sizing, topology design and layout problems. Compared to previous optimization-based algorithms, machine learning methods require fewer simulation rounds but achieve higher quality designs. However, existing methods cannot replace human experts yet in the analog design flow. One obstacle is that the models are learned from a limited dataset and have limited flexibility. Most researchers train and test their method on typical circuits like OTAs. A generalizable model designed for a variety of circuits is desired in the future study. Another challenge is that the vast space of system-level design has not been studied. The potential of machine learning in analog design may be further exploited in the future.

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